WO2020263875A1 - Détection de faux doigt à l'aide de caractéristiques de crête - Google Patents

Détection de faux doigt à l'aide de caractéristiques de crête Download PDF

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Publication number
WO2020263875A1
WO2020263875A1 PCT/US2020/039208 US2020039208W WO2020263875A1 WO 2020263875 A1 WO2020263875 A1 WO 2020263875A1 US 2020039208 W US2020039208 W US 2020039208W WO 2020263875 A1 WO2020263875 A1 WO 2020263875A1
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WIPO (PCT)
Prior art keywords
image
finger
signal strength
fingerprint
difference
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PCT/US2020/039208
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English (en)
Inventor
Sina AKHBARI
Lingtao Wang
Mei-Lin Chan
Nikhil APTE
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Invensense, Inc.
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Publication date
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Publication of WO2020263875A1 publication Critical patent/WO2020263875A1/fr

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Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1382—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger
    • G06V40/1394—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger using acquisition arrangements
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/13—Sensors therefor
    • G06V40/1306—Sensors therefor non-optical, e.g. ultrasonic or capacitive sensing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1347—Preprocessing; Feature extraction
    • G06V40/1359—Extracting features related to ridge properties; Determining the fingerprint type, e.g. whorl or loop
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
    • G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
    • G06V40/12—Fingerprints or palmprints
    • G06V40/1382—Detecting the live character of the finger, i.e. distinguishing from a fake or cadaver finger

Definitions

  • Fingerprint sensors have become ubiquitous in mobile devices as well as other devices (e.g., locks on cars and buildings) and applications for authenticating a user’s identity. They provide a fast and convenient way for the user to unlock a device, provide authentication for payments, etc. It is essential that fingerprint sensors operate at a level of security that, at a minimum, reduces the potential for circumvention of security of fingerprint authentication. For instance, fake fingers having fake or spoofed fingerprints can be used to attempt to circumvent fingerprint authentication at fingerprint sensors.
  • FIG. 1 is a block diagram of an example electronic device 100 upon which
  • FIG. 2 illustrates a block diagram of an example fingerprint sensing system for determining whether a fingerprint image was generated using a real finger or a fake finger, according to some embodiments.
  • FIGs. 3A and 3B illustrate block diagrams of a difference quantifier, according to some embodiments.
  • FIG. 4 illustrates example fingerprint images captured at an ultrasonic fingerprint sensor using different times of flight, according to embodiments.
  • FIG. 5A illustrates example fingerprint images and graphs of spatial ridge frequency of a real finger, according to embodiments.
  • FIG. 5B illustrates example fingerprint images and graphs of spatial ridge frequency of a fake finger, according to embodiments.
  • FIG. 6A illustrates example fingerprint images and a combined graph of spatial ridge frequency of a real finger, according to embodiments.
  • FIG. 6B illustrates example fingerprint images and a combined graph of spatial ridge frequency of a fake finger, according to embodiments.
  • FIG. 7 illustrates an example process for determining whether a finger is a real finger at an ultrasonic fingerprint sensor, according to some embodiments.
  • FIG. 8 illustrates an example process for quantifying a difference in width of ridges of different fingerprint images, according to some embodiments.
  • FIG. 9 illustrates an example process for quantifying a difference in width of ridges of different fingerprint images, according to other embodiments.
  • Embodiments described herein may be discussed in the general context of processor- executable instructions residing on some form of non-transitory processor-readable medium, such as program modules, executed by one or more computers or other devices.
  • program modules include routines, programs, objects, components, data structures, etc., that perform particular tasks or implement particular abstract data types.
  • the functionality of the program modules may be combined or distributed as desired in various embodiments.
  • a single block may be described as performing a function or functions; however, in actual practice, the function or functions performed by that block may be performed in a single component or across multiple components, and/or may be performed using hardware, using software, or using a combination of hardware and software.
  • various illustrative components, blocks, modules, logic, circuits, and steps have been described generally in terms of their functionality. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the overall system. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present disclosure.
  • the example fingerprint sensing system and/or mobile electronic device described herein may include components other than those shown, including well-known components.
  • Various techniques described herein may be implemented in hardware, software, firmware, or any combination thereof, unless specifically described as being implemented in a specific manner. Any features described as modules or components may also be implemented together in an integrated logic device or separately as discrete but interoperable logic devices if implemented in software, the techniques may be realized at least in part by a non-transitory processor-readable storage medium comprising instructions that, when executed, perform one or more of the methods described herein.
  • the non-transitory processor-readable data storage medium may form part of a computer program product, which may include packaging materials.
  • the non-transitory processor-readable storage medium may comprise random access memory (RAM) such as synchronous dynamic random access memory (SDRAM), read only memory (ROM), non-volatile random access memory (NVRAM), electrically erasable programmable read-only memory (EERROM), FLASH memory, other known storage media, and the like.
  • RAM synchronous dynamic random access memory
  • ROM read only memory
  • NVRAM non-volatile random access memory
  • EERROM electrically erasable programmable read-only memory
  • FLASH memory other known storage media, and the like.
  • the techniques additionally, or alternatively, may be realized at least in part by a processor-readable communication medium that carries or communicates code in the form of instructions or data structures and that can be accessed, read, and/or executed by a computer or other processor.
  • processors such as one or more motion processing units (MPUs), sensor processing units (SPUs), host processor(s) or core(s) thereof, digital signal processors (DSPs), general purpose
  • MPUs motion processing units
  • SPUs sensor processing units
  • DSPs digital signal processors
  • processors application specific integrated circuits (ASICs), application specific instruction set processors (AS!Ps), field programmable gate arrays (FPGAs), a programmable logic controller (PLC), a complex programmable logic device (CPLD), a discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein, or other equivalent integrated or discrete logic circuitry.
  • ASICs application specific integrated circuits
  • AS!Ps application specific instruction set processors
  • FPGAs field programmable gate arrays
  • PLC programmable logic controller
  • CPLD complex programmable logic device
  • discrete gate or transistor logic discrete hardware components, or any combination thereof designed to perform the functions described herein, or other equivalent integrated or discrete logic circuitry.
  • processor can refer to substantially any computing processing unit or device comprising, but not limited to comprising, single-core processors; single-processors with software multithread execution capability; multi-core processors; multi-core processors with software multithread execution capability; multi-core processors with hardware multithread technology; parallel platforms; and parallel platforms with distributed shared memory.
  • processors can exploit nano-scale architectures such as, but not limited to, molecular and quantum-dot based transistors, switches and gates, in order to optimize space usage or enhance performance of user equipment.
  • a processor may also be implemented as a combination of computing processing units.
  • a general purpose processor may be a microprocessor, but in the alternative, the processor may be any conventional processor, controller, microcontroller, or state machine.
  • a processor may also be implemented as a combination of computing devices, e.g., a combination of an SPU/MPU and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with an SPU core, MPU core, or any other such configuration.
  • Discussion begins with a description of a device including a fingerprint sensor, upon which described embodiments can be implemented.
  • An example fingerprint sensor and system for determining whether a fingerprint image is generated using a real finger or a fake finger is then described, in accordance with various embodiments.
  • Example operations of a fingerprint sensor for determining whether a fingerprint image is generated using a real finger or a fake finger based on features of finger ridges of the captured fingerprint images are then described.
  • Fingerprint sensors are used in electronic devices for user authentication, such as mobile electronic devices and applications operating on mobile electronic devices, locks for accessing cars or buildings, for protecting against unauthorized access to the devices and/or applications. Authentication of a fingerprint at a fingerprint sensor is performed before providing access to a device and/or application. In order to circumvent fingerprint authentication, attempts can be made to copy or spoof fingerprints of an authorized user using a fake or artificial finger. As such, fingerprint sensors should be capable of distinguishing real fingers from fake, artificial, or even dead fingers, also referred to herein as performing“spoof detection” or“fake finger detection”. A“spoofed” fingerprint is a fake or artificial fingerprint that is used to attempt to circumvent security measures requiring fingerprint authentication.
  • an artificial finger may be used to gain unauthorized access to the electronic device or application, by making an unauthorized copy of the fingerprint of an authorized user, e.g.,“spoofing” an actual fingerprint.
  • the spoof detection may be performed by analyzing fingerprint images captured by the fingerprint sensor, e.g., performing biometric analysis of the fingerprint images, or iooking at any characteristics that can help distinguish a fake/spoof fingerprint from a real fingerprint. These characteristics may be static features or dynamic features which have a certain time dependency because they change over time.
  • Embodiments described herein provide methods and systems for determining whether a finger interacting with a fingerprint sensor, for purposes of authentication, is a real finger or a fake finger based on observed features of finger ridges of the captured fingerprint images.
  • Observed features of finger ridges may refer to the width of ridges of the ridge/valley pattern of captured fingerprint images.
  • a feature of the finger ridge may include a profile of the ridge, and how the profile changes based on depth and/or deformation.
  • capturing multiple fingerprint images using different times of flight allows for detecting ridge features that are indicative of whether a finger is a real finger or a fake finger.
  • Embodiments described herein provide for determining whether a finger is a real finger at an ultrasonic fingerprint sensor.
  • a first image of a fingerprint pattern is captured at an ultrasonic fingerprint sensor, wherein the first image is based on ultrasonic signals corresponding to a first time of flight range.
  • a second image of the fingerprint pattern is captured at the ultrasonic fingerprint sensor, wherein the second image is based on ultrasonic signals corresponding to a second time of flight range, the second time of flight range being delayed compared to the first time of flight range.
  • a difference in a width of ridges of the fingerprint pattern in the first image compared to the width of ridges of the fingerprint pattern in the second image is quantified. Based on the quantification of the difference, a probability whether the finger is a real finger is determined.
  • quantifying the difference in the width of the ridges of the fingerprint pattern in the first image compared to the width of ridges of the fingerprint pattern in the second image includes determining a difference between the first image and the second image in one embodiment, a difference image is generated by subtracting the second image from the first image (or vice-versa).
  • a first signal strength is determined at a first spatial frequency range of at least one of the first image and the second image and a second signal strength is determined at a second spatial frequency range of the difference between the first image and the second image.
  • the first signal strength corresponds to a maximum in signal strength of the first spatial frequency range and wherein the second signal strength corresponds to a maximum in signal strength of the second spatiai frequency range.
  • the second frequency range is distributed around a frequency substantially double a main frequency contribution of the first frequency range.
  • the first spatial frequency range corresponds to a spatial ridge frequency range of the fingerprint pattern.
  • the first signal strength is compared to the second signal strength.
  • a ratio of the second signal strength to the first signal strength is determined. Provided the ratio satisfies a ratio range threshold, it is determined that the finger is a real finger in one embodiment, the ratio range threshold is above 0.8. Based on the comparing, the probability that the finger is a real finger is determined. In one embodiment, the probability that the finger is a real finger is based on the ratio of the second signal strength to the first signal strength, wherein the probability that the finger is a real finger increases as the ratio of the second signal strength to the first signal strength increases.
  • determining the second signal strength at the second spatial frequency range of the difference between the first image and the second image includes determining the second signal strength at the second spatial frequency of the of the difference image.
  • quantifying the difference in the width of the ridges of the fingerprint pattern in the first image compared to the width of ridges of the fingerprint pattern in the second image includes determining a first average width of ridges of the fingerprint pattern of the first image and determining a second average width of ridges of the fingerprint pattern of the second image.
  • a difference between the first average width and the second average width is quantified in some embodiments, a ratio of the first average width to the second average width is determined.
  • determining the probability whether the finger is a real finger includes comparing of the ratio of the first average width and the second average width to a width range threshold.
  • FIG. 1 is a block diagram of an example electronic device 100.
  • electronic device 100 may be implemented as a device or apparatus, such as a handheld mobile electronic device.
  • a mobile electronic device may be, without limitation, a mobile telephone phone (e.g., smartphone, cellular phone, a cordless phone running on a local network, or any other cordless telephone handset), a wired telephone (e.g., a phone attached by a wire), a personal digital assistant (PDA), a video game player, video game controller, a Head Mounted Display (HMD), a virtual or augmented reality device, a navigation device, an activity or fitness tracker device (e.g , bracelet, clip, band, or pendant), a smart watch or other wearable device, a mobile internet device (MID), a personal navigation device (PND), a digital still camera, a digital video camera, a portable music player, a portable video player, a portable multi-media player, a remote control,
  • MID mobile internet device
  • PND personal navigation device
  • electronic device 100 may be implemented as a fixed electronic device, such as and without limitation, an electronic lock, a doorknob, a car start button, an automated teller machine (ATM), etc.
  • electronic device 100 is capable of reading fingerprints.
  • electronic device 100 may include a host processor 110, a host bus 120, a host memory 130, and a sensor processing unit 170. Some embodiments of electronic device 100 may further include one or more of a display device 140, an interface 150, a transceiver 160 (ail depicted in dashed lines) and/or other components. In various embodiments, a display device 140, an interface 150, a transceiver 160 (ail depicted in dashed lines) and/or other components. In various
  • electrical power for electronic device 100 is provided by a mobile power source such as a battery (not shown), when not being actively charged.
  • Host processor 110 can be one or more microprocessors, central processing units (CPUs), DSPs, general purpose microprocessors, ASICs, ASIPs, FPGAs or other processors which run software programs or applications, which may be stored in host memory 130, associated with the functions and capabilities of electronic device 100.
  • USB universal serial bus
  • UART universal asynchronous receiver/transmitter
  • microcontroller bus architecture ABA
  • I2C Inter-Integrated Circuit
  • SDIO serial digital input output
  • SPI serial peripheral interface
  • host processor 110, host memory 130, display 140, interface 150, transceiver 160, sensor processing unit (SPU) 170, and other components of electronic device 100 may be coupled communicatively through host bus 120 in order to exchange commands and data.
  • different bus configurations may be employed as desired.
  • additional buses may be used to couple the various components of electronic device 100, such as by using a dedicated bus between host processor 110 and memory 130.
  • Host memory 130 can be any suitable type of memory, including but not limited to electronic memory (e.g., read only memory (ROM), random access memory, or other electronic memory), hard disk, optical disk, or some combination thereof.
  • electronic memory e.g., read only memory (ROM), random access memory, or other electronic memory
  • hard disk e.g., hard disk, optical disk, or some combination thereof.
  • Multiple layers of software can be stored in host memory 130 for use with/operation upon host processor 110.
  • an operating system layer can be provided for electronic device 100 to control and manage system resources in real time, enable functions of application software and other layers, and interface application programs with other software and functions of electronic device 100.
  • a user experience system layer may operate upon or be facilitated by the operating system.
  • the user experience system may comprise one or more software application programs such as menu navigation software, games, device function control, gesture recognition, image processing or adjusting, voice recognition, navigation software, communications software (such as telephony or wireless local area network (WLAN) software), and/or any of a wide variety of other software and functional interfaces for interaction with the user can be provided.
  • multiple different applications can be provided on a single electronic device 100, and in some of those embodiments, multiple applications can run simultaneously as part of the user experience system.
  • the user experience system, operating system, and/or the host processor 110 may operate in a low-power mode (e.g., a sleep mode) where very few instructions are processed. Such a low-power mode may utilize only a small fraction of the processing power of a full-power mode (e.g., an awake mode) of the host processor 110.
  • Display 140 when included, may be a liquid crystal device, (organic) light emitting diode device, or other display device suitable for creating and visibly depicting graphic images and/or alphanumeric characters recognizable to a user.
  • Display 140 may be configured to output images viewable by the user and may additionally or alternatively function as a viewfinder for camera. It should be appreciated that display 140 is optional, as various electronic devices, such as electronic locks, doorknobs, car start buttons, etc., may not require a display device.
  • Interface 150 when included, can be any of a variety of different devices providing input and/or output to a user, such as audio speakers, touch screen, real or virtual buttons, joystick, slider, knob, printer, scanner, computer network I/O device, other connected
  • Transceiver 160 when included, may be one or more of a wired or wireless
  • transceiver which facilitates receipt of data at electronic device 100 from an external
  • transceiver 160 comprises one or more of: a cellular transceiver, a wireless local area network transceiver (e.g., a transceiver compliant with one or more Institute of Electrical and Electronics Engineers (IEEE) 802.11 specifications for wireless local area network communication), a wireless personal area network transceiver (e.g., a transceiver compliant with one or more IEEE 802.15 specifications for wireless personal area network communication), and a wired a serial transceiver (e.g., a universal serial bus for wired communication).
  • IEEE Institute of Electrical and Electronics Engineers
  • Electronic device 100 also includes a general purpose sensor assembly in the form of integrated Sensor Processing Unit (SPU) 170 which includes sensor processor 172, memory 176, a fingerprint sensor 178, and a bus 174 for facilitating communication between these and other components of SPU 170.
  • SPU 170 may include at least one additional sensor 180 (shown as sensor 180-1 , 180-2, ... 180-n) communicatively coupled to bus 174.
  • at least one additional sensor 180 is a force or pressure sensor (e.g. a touch sensor) configured to determine a force or pressure or a temperature sensor configured to determine a temperature at electronic device 100.
  • the force or pressure sensor may be disposed within, under, or adjacent fingerprint sensor 178. in some
  • SPU 170 all of the components illustrated in SPU 170 may be embodied on a single integrated circuit it should be appreciated that SPU 170 may be manufactured as a stand alone unit (e.g., an integrated circuit), that may exist separately from a larger electronic device and is coupled to host bus 120 through an interface (not shown). It should be appreciated that, in accordance with some embodiments, that SPU 170 can operate independent of host processor 110 and host memory 130 using sensor processor 172 and memory 176.
  • Sensor processor 172 can be one or more microprocessors, CPUs, DSPs, general purpose microprocessors, ASICs, ASIPs, FPGAs or other processors which run software programs, which may be stored in memory 176, associated with the functions of SPU 170. It should also be appreciated that fingerprint sensor 178 and additional sensor 180, when included, may also utilize processing and memory provided by other components of electronic device 100, e.g., host processor 110 and host memory 130
  • Bus 174 may be any suitable bus or interface to include, without limitation, a peripheral component interconnect express (PCIe) bus, a universal serial bus (USB), a universal asynchronous receiver/transmitter (UART) serial bus, a suitable advanced microcontroller bus architecture (AMBA) interface, an Inter- Integrated Circuit (I2C) bus, a serial digital input output (SDIO) bus, a serial peripheral interface (SRI) or other equivalent.
  • PCIe peripheral component interconnect express
  • USB universal serial bus
  • UART universal asynchronous receiver/transmitter
  • AMBA advanced microcontroller bus architecture
  • I2C Inter- Integrated Circuit
  • SDIO serial digital input output
  • SRI serial peripheral interface
  • sensor processor 172 may be communicatively coupled through bus 174 in order to exchange data.
  • memory 176 may be communicatively coupled through bus 174 in order to exchange data.
  • fingerprint sensor 178 may be communicatively coupled through bus 174 in order to exchange data.
  • Memory 176 can be any suitable type of memory, including but not limited to electronic memory (e.g., read only memory (ROM), random access memory, or other electronic memory). Memory 176 may store algorithms or routines or other instructions for processing data received from fingerprint sensor 178 and/or one or more sensor 180, as well as the received data either in its raw form or after some processing. Such algorithms and routines may be implemented by sensor processor 172 and/or by logic or processing capabilities included in fingerprint sensor 178 and/or sensor 180.
  • ROM read only memory
  • Memory 176 may store algorithms or routines or other instructions for processing data received from fingerprint sensor 178 and/or one or more sensor 180, as well as the received data either in its raw form or after some processing. Such algorithms and routines may be implemented by sensor processor 172 and/or by logic or processing capabilities included in fingerprint sensor 178 and/or sensor 180.
  • a sensor 180 may comprise, without limitation: a temperature sensor, a humidity sensor, an atmospheric pressure sensor, an infrared sensor, a radio frequency sensor, a navigation satellite system sensor (such as a global positioning system receiver), an acoustic sensor (e.g., a microphone), an inertial or motion sensor (e.g., a gyroscope, accelerometer, or magnetometer) for measuring the orientation or motion of the sensor in space, or other type of sensor for measuring other physical or environmental factors.
  • sensor 180-1 may comprise an acoustic sensor
  • sensor 180-2 may comprise a temperature sensor
  • sensor 180-n may comprise a motion sensor
  • fingerprint sensor 178 and/or one or more sensors 180 may be implemented using a microelectromechanical system (MEMS) that is integrated with sensor processor 172 and one or more other components of SPU 170 in a single chip or package. It should be appreciated that fingerprint sensor 178 may be disposed behind display 140.
  • MEMS microelectromechanical system
  • fingerprint sensor 178 and/or one or more sensors 180 may be disposed externally to SPU 170 in various embodiments.
  • fingerprint sensor 178 can be any type of fingerprint sensor, including without limitation, an ultrasonic sensor, an optical sensor, a camera, etc.
  • FIG. 2 illustrates a block diagram of an example fingerprint sensing system 200 for determining whether a fingerprint image was generated using a real finger or a fake finger, according to some embodiments.
  • Fingerprint sensing system 200 is configured to determine whether a finger is a real finger or a fake finger using ridge features (e.g., width) from fingerprint images captured at the ultrasonic fingerprint sensor it should be appreciated that fingerprint sensing system 200 can be implemented as hardware, software, or any combination thereof it should also be appreciated that fingerprint image capture 210, difference quantifier 220, and fake finger determiner 230 may be separate components, may be comprised within a single component, or may be comprised in various combinations of multiple components (e.g., difference quantifier 220 and fake finger determiner 230 may be comprised within a single component), In accordance with some embodiments.
  • Fingerprint images 215 are captured at fingerprint image capture 210.
  • fingerprint image capture 210 is an ultrasonic sensor (e.g., a sensor capable of transmitting and receiving ultrasonic signals).
  • the fingerprint sensor is operable to emit and detect ultrasonic waves (also referred to as ultrasonic signals or ultrasound signals).
  • An array of ultrasonic transducers e.g., Piezoelectric
  • Micromachined Ultrasonic Transducers may be used to transmit and receive the ultrasonic waves, where the ultrasonic transducers of the array are capable of performing both the transmission and receipt of the ultrasonic waves.
  • the emitted ultrasonic waves are reflected from any objects in contact with (or in front of) the fingerprint sensor, and these reflected ultrasonic waves, or echoes, are then detected.
  • the object is a finger
  • the waves are reflected from different features of the finger, such as the surface features on the skin, fingerprint, or features present in deeper layers of the finger (e.g., the dermis).
  • Examples of surface features of a finger are ridges and valleys of a fingerprint, e.g., the ridge/valley pattern of the finger.
  • the reflection of the sound waves from the ridge/valley pattern enables the fingerprint sensor to produce a fingerprint image that may be used for identification of the user.
  • At least two fingerprint images 215 are captured at an ultrasonic fingerprint sensor at different times of flight
  • operating parameters of an ultrasonic fingerprint sensor can be controlled, allowing for image capture at different times of flight.
  • an adjustment of timing of transmission of the ultrasonic signals for ultrasonic transducers of an ultrasonic fingerprint sensor can change the time of flight.
  • a first fingerprint image is captured at a finger surface time of flight (e.g., a time of flight formed for imaging at a contact surface of the ultrasonic transducer) and second fingerprint image is captured at a delayed time of flight (e.g., 50-150 nanoseconds) relative to the first image.
  • the finger used for generating the fingerprint images is a real finger, it is typically observed (either visually or analytically) that ridges of the second fingerprint image are narrower than ridges of the first fingerprint image. This event (ridge narrowing) is typically not observed in fake fingers.
  • the time of flight delay may be selected for an optimum ridge narrowing effect, and may be dependent on the user. As such, the delay in time of flight may be determined for user during enrollment.
  • the first image is captured with the optimal time of flight (e.g., calibrated time of flight for an optimum image) for measuring at the sensor surface, and the signal integration windows may be optimized for the fake finger detection.
  • the integration window may be large (e.g., 50-2Q0ns), while for the fake finger detection a shorter integration window (e.g., ⁇ 50ns) may be used.
  • the signal integration window for the first and second image may be different.
  • Embodiments described herein focus on the use of a first and second image. However, more images may be used at different time of flights, and the methods described herein may then be applied in a similar manner on the plurality of images.
  • the shorter integration window provides more depth resolution. It should be appreciated that the actual integration windows may depend on the ultrasonic fingerprint sensor stack, thickness and material, and acoustic properties of the specific ultrasonic fingerprint sensor design.
  • the beam focusing for the two images may be the same, and may be adapted to focus on the top of the sensor stack. In other embodiments, the beam focusing may be different, where the focusing for the second image is at certain depth in the finger or certain depth from the sensor surface.
  • the capturing of the first and second images may be initiated whenever a change in signal is detected. For example, to capture the images when the user presses the finger on the sensor, the image capture may be started as soon an object or finger starts interacting with the sensor. For an ultrasonic sensor with an array of ultrasonic transducers, a subset of transducers may be active in a low power mode, and as soon as a finger start interacting with the sensor, the full sensor may be activated to capture the sequence of images.
  • a change in signal may occur as the pressure of the finger is reduced, and this may initiate the image capture in some embodiments, a background image is captured before a finger contacts the sensor, where the background image can be used to enhance image quality by subtracting from the captured fingerprint images.
  • the change of contact state may be determined by the fingerprint sensor itself, or it may be detected by a second sensor associated with the fingerprint sensor.
  • a pressure sensor, a force sensor, or a touch sensor may be position near, below, or above the fingerprint sensor and this additional sensor may be used to detect a change in contact state that initiates the capturing of the image sequence.
  • the fingerprint images 215 can include any number of fingerprint images. In some embodiments, two fingerprint images 215 are captured using different times of flight In some embodiments, fingerprint images 215 includes at least two fingerprint images, but it should be appreciated that more than two image can be captured.
  • the fingerprint images 215 forwarded to difference quantifier 220 may include two or more images captured during a steady state while the finger is contacting the ultrasonic fingerprint sensor. The embodiments described herein may then be applied in a similar manner on the plurality of images to determine a change in ridge width as a function of time of flight.
  • Fingerprint images 215 are received at difference quantifier 220, which is configured to quantify a difference of ridge features (e.g , ridge width) of the fingerprint images 215.
  • the (difference of) ridge features may be determined as a function of time of flight. For instance, ridge narrowing as a function of time of flight can be taken as an indicator for the probability that the finger is a real or fake finger. In general, the depth profile of the ridges can be used as an indicator as to the probability that the finger is a real or fake finger. To determine the probability, embodiments herein quantify the ridge narrowing (e.g., profile change).
  • FIG. 3A illustrates a block diagram of a difference quantifier 220, according to an embodiment.
  • Fingerprint images 215 are received at difference quantifier 220.
  • difference determiner 310 a difference between the fingerprint images is determined in one embodiment, difference determiner includes difference image generator 312, which is configured to generate a difference image by subtracting one of the fingerprint images 215 from another fingerprint image 215. In one embodiment, where there are two fingerprint images 215 captured with different times of flight, the latter captured fingerprint image 215 is subtracted from the earlier captured fingerprint image 215.
  • Difference determiner 310 outputs difference 315.
  • difference 315 is a difference image.
  • the second image is subtracted from the first image, and the spatial frequency spectrum of the first image and of the difference image is compared. If the finger is real, due to ridge narrowing of the second image, the ridges of the difference image are observably split. Therefore, the difference image has substantially twice the main spatial frequency compared to the first and second images alone.
  • Signal strength determiner 320 receives difference 315 and at least one of fingerprint images 215, and is configured to determine signal strengths corresponding to spatial frequency values or ranges. For example, signal strength determiner 320 determines a maximum signal strength at a first spatial frequency of at least one of fingerprint images 215 and a maximum signal strength at a second spatial frequency of difference 315. The first spatial frequency of each of the fingerprint images 215 should be substantially the same. In some embodiments, the second spatial frequency range is distributed around a frequency substantially double a main frequency contribution of the first spatial frequency range. In some embodiments, signal strength determiner 320 determines the maximum signal strength for at least one of fingerprint images 215, and identifies the corresponding spatial frequency (within the first spatial frequency range). Signal strength determiner 320 then determines the maximum signal strength for difference 315 at the second spatial frequency range, where the second spatial frequency range is distributed around a frequency substantially double the frequency contribution of the first frequency range.
  • the signal strengths 325 are output to signal strength comparer 330.
  • Signal strength comparer 330 is configured to compare the signal strength 325 for at least fingerprint image 215 and difference 315.
  • Output 335 is generated as a result of the comparison, where output 335 includes a probability whether the finger is a real finger or a fake finger.
  • the ratio of the signal strength of difference 315 to the signal strength of a fingerprint image 215 is determined. The ratio is then compared to a ratio range threshold.
  • the probability that the finger is a real finger is based on the ratio, wherein the probability that the finger is a real finger increases as the ratio increases.
  • the ratio range threshold is greater than 0 8.
  • output 335 is transmitted to fake finger determiner 230 as difference quantification 225.
  • FIG. 3B illustrates a block diagram of difference quantifier 220, according to another embodiment. Fingerprint images 215 are received at difference quantifier 220. At ridge width determiner 360, the average ridge widths 365 of each of fingerprint images 215 is generated.
  • Ridge width difference quantifier 370 receives ridge widths 365 and quantifies a difference between the ridge widths 365. The change of width may be determined as a function of the time of flight. In one embodiments, ridge width difference quantifier 370 determines a ratio 375 of the ridge widths 365. Ratio comparer 380 receives ratio 375, and compares the ratio to a width range threshold. In some embodiments, the probability that the finger is a real finger is based on the ratio. In some embodiments, output 385 is transmitted to fake finger determiner 230 as difference quantification 225.
  • difference quantification 225 is output 335 of FIG. 3A. In other embodiments, difference quantification 225 is output 385 of FIG. 3B. In some embodiments, difference quantification includes a probability whether the finger is a real finger or a fake finger. In some embodiments, fake finger determiner 230 receives input from other types of spoof detection or fake finger detection, and makes the determination 235 as to whether the finger is a fake finger based on multiple inputs.
  • Each type of spoof detection method may produce a probability of whether the finger is a real finger, and fake finger determiner may combine the probabilities to output a decision whether the finger is a real finger.
  • the output may also contain a confidence, based on the one or more spoof detection methods.
  • the different probabilities from different methods may have different weights, which may depend on the user. These weights may have been determined in during enrollment, where it may be determined which spoof detection works best for the user, and may even depend on the context.
  • FIG. 4 illustrates example fingerprint images 410 and 420 of a real finger captured at an ultrasonic fingerprint sensor using different times of flight, according to embodiments.
  • Fingerprint image 410 is captured at a first time of flight, where the first time of flight is the standard (e.g., calibrated) time of flight for capturing an image at a surface of the ultrasonic fingerprint sensor.
  • Fingerprint image 420 is captured at a second time of flight, where the second time of flight is delayed (e.g., 15 to 150 nanoseconds) relative to the first time of flight.
  • the ridges of fingerprint images 410 and 420 are different, indicating a change in ridge profile (e.g., ridge width), with the ridges narrowing in fingerprint image 420 relative to fingerprint image 410.
  • the observed width of the ridges narrows at the delayed time of flight in fingerprint image 420.
  • the ridge of the fingerprint pattern are dark in the image and the valleys are white, because the ultrasonic signals are more absorbed at the ridges and more reflected at the valley due to the air gap present at the position of the valleys in some embodiments, the ridge profile changes illustrating ridge narrowing at delayed times of flight can be used to determine whether the finger is a real finger or a fake finger.
  • FIG. 5A illustrates example 500 fingerprint images and graphs of spatial ridge frequency of a real finger, according to embodiments.
  • Example 500 illustrates first fingerprint image 510, second fingerprint image 520, and difference image 530, of a real finger, where the ridges are indicated in the fingerprint images as the darker patterns.
  • First fingerprint image 510 is captured at a first time of flight
  • second fingerprint image 520 is captured at a delayed time of flight relative to the first time of flight.
  • Difference image 530 is generated by subtracting second fingerprint image 520 from first fingerprint image 510.
  • Graph 515 illustrates a graph of spectrogram power (e.g., signal strength) versus frequency of first fingerprint image 510, in which the maximum power corresponds to a spatial frequency range centered at approximately 2.4 line pairs per millimeter (Ipmm).
  • Graph 525 illustrates a graph of spectrogram power versus frequency of second fingerprint image 520, in which the maximum power also corresponds to a spatial frequency range centered at approximately 2.4 Ipmm.
  • Graph 535 illustrates a graph of spectrogram power versus frequency of difference image 530, in which the maximum power corresponds to a spatial frequency range centered at approximately 4.8 Ipmm.
  • the frequency of detected ridges in difference image 530 is substantially double the frequency of detected ridges in first fingerprint image 510 and second fingerprint image 520, indicative of the finger being a real finger.
  • the finger is a real finger
  • the ridges of difference image 530 are observably split. Therefore, difference image 530 has substantiaily twice the main spatial frequency comparing to one of first fingerprint image 510 and second fingerprint image 520. Therefore, example 500 illustrates that first fingerprint image 510 and second fingerprint image 520 were generated using a real finger.
  • the ratio of the signal strength of difference image 530 to the signal strength of one of first fingerprint image 510 and second fingerprint image 520 is determined. The ratio is then compared to a ratio range threshold. In some embodiments, the probability that the finger is a real finger is based on the ratio, wherein the probability that the finger is a real finger increases as the ratio increases. The reasoning is that for real fingers the change in width as a function of time of flight is more observable, and therefore the signal strength of the second spatial frequency range is higher.
  • FIG. 5B illustrates example 550 fingerprint images and graphs of spatial ridge frequency of a fake finger, according to embodiments.
  • Example 550 illustrates first fingerprint image 580, second fingerprint image 570, and difference image 580, of a fake finger, where the ridges are indicated in the fingerprint images as the darker patterns.
  • First fingerprint image 560 is captured at a first time of flight
  • second fingerprint image 570 is captured at a delayed time of flight relative to the first time of flight.
  • Difference image 580 is generated by subtracting second fingerprint image 570 from first fingerprint image 560.
  • Graph 565 illustrates a graph of spectrogram power versus frequency of first fingerprint image 560, in which the maximum power corresponds to a spatial frequency range centered at approximately 2.1 line pairs per millimeter (Ipmm).
  • Graph 575 illustrates a graph of spectrogram power versus frequency of second fingerprint image 570, in which the maximum power also corresponds to a spatial frequency range centered at approximately 2.1 Ipmm.
  • Graph 585 illustrates a graph of spectrogram power versus frequency of difference image 580, in which the maximum power at around twice the main frequency contributions of the first and/or second image is difficult to determine.
  • the frequency of detected ridges in difference image 580 is not substantially double the frequency of detected ridges in first fingerprint images 560 or second fingerprint image 570, indicative of the finger being a fake finger. Moreover, there is no maximum or peak signal strength at or near 4.2 Ipmm of difference image 580 Where the finger is a fake finger, the ridges of difference image 580 are not clearly observable. In particular, there is no observable ridge splitting in difference image 580, and no observable ridge narrowing between first fingerprint images 560 and second fingerprint image 570. Therefore, example 550 illustrates that first fingerprint images 560 and second fingerprint image 570 were generated using a fake finger.
  • FIG. 6A illustrates example 600 of fingerprint images and a composite graph of spatial ridge frequency of a real finger, according to embodiments.
  • Example 600 illustrates first fingerprint image 610, second fingerprint image 620, and difference image 630, of a real finger, where the ridges are indicated in the fingerprint images as the darker patterns.
  • First fingerprint image 610 is captured at a first time of flight
  • second fingerprint image 620 is captured at a delayed time of flight relative to the first time of flight.
  • Difference image 630 is generated by subtracting second fingerprint image 620 from first fingerprint image 610.
  • Graph 615 illustrates a graph of normalized power (e.g., signal strength) versus frequency of first fingerprint image 610, second fingerprint image 620, and difference image 630.
  • First fingerprint image 610 corresponds to a ridge frequency of approximately 2.5 Ipmm, as indicated at point 614 of line 612.
  • Second fingerprint image 620 corresponds to a ridge frequency of approximately 2.3 Ipmm, as indicated at point 624 line 622.
  • Difference image 630 corresponds to a maximum ridge frequency of approximately 4.6 Ipmm as indicated at point 634 of line 632.
  • the frequency of detected ridges in difference image 630 is substantially double the frequency of detected ridges in first fingerprint image 610 and second fingerprint image 620, indicative of the finger being a real finger.
  • the finger is a real finger
  • the ridges of difference image 630 are observably split. Therefore, difference image 630 has substantially twice the main spatial frequency comparing to one of first fingerprint image 610 and second fingerprint image 620. Therefore, example 600 illustrates that first fingerprint image 610 and second fingerprint image 620 were generated using a real finger.
  • the ratio of the signal strength of difference image 630 to the signal strength of one of first fingerprint image 610 and second fingerprint image 620 is determined. The ratio is then compared to a ratio range threshold. In some embodiments, the probability that the finger is a real finger is based on the ratio, wherein the probability that the finger is a real finger increases as the ratio increases. The reasoning is that for real fingers the change in width as a function of time of flight is more observable, and therefore the signal strength of the second spatial frequency range is higher.
  • FIG. 6B illustrates example 650 of fingerprint images and a composite graph of spatial ridge frequency of a fake finger, according to embodiments.
  • Example 650 illustrates first fingerprint image 660, second fingerprint image 670, and difference image 680, of a fake finger, where the ridges are indicated in the fingerprint images as the darker patterns.
  • First fingerprint Image 660 is captured at a first time of flight
  • second fingerprint image 670 is captured at a delayed time of flight relative to the first time of flight.
  • Difference image 680 is generated by subtracting second fingerprint image 670 from first fingerprint image 660.
  • Graph 690 illustrates a graph of normalized power (e.g., signal strength) versus frequency of first fingerprint image 660, second fingerprint image 670, and difference image 680
  • First fingerprint image 660 and second fingerprint image 670 correspond to a ridge frequency of approximately 2.2 ipmm, as indicated at point 664 of line 662 and point 674 of line 672, respectively.
  • Difference image 680 corresponds to a maximum ridge frequency of approximately 1.4 Ipmm, as indicated at point 684 of line 682.
  • the frequency of detected ridges in difference image 680 is not substantially double the frequency of detected ridges in first fingerprint image 660 or second fingerprint image 670, indicative of the finger being a fake finger.
  • example 650 illustrates that first fingerprint image 660 and second fingerprint image 670were generated using a fake finger.
  • FIGs. 7 through 9 illustrate example processes for determining whether a finger is a real finger at a fingerprint sensor, according to some embodiments. Procedures of the example processes will be described with reference to elements and/or components of various figures described herein. It is appreciated that in some embodiments, the procedures may be performed in a different order than described, that some of the described procedures may not be performed, and/or that one or more additional procedures to those described may be performed.
  • the flow diagram includes some procedures that, in various embodiments, are carried out by one or more processors (e.g., a host processor or a sensor processor) under the control of computer-readable and computer-executable instructions that are stored on non- transitory computer-readable storage media it is further appreciated that one or more procedures described in the flow diagrams may be implemented in hardware, or a combination of hardware with firmware and/or software.
  • processors e.g., a host processor or a sensor processor
  • a first image of a fingerprint pattern is captured at an ultrasonic fingerprint sensor, wherein the first image is based on ultrasonic signals corresponding to a first time of flight range.
  • a second image of the fingerprint pattern is captured at the ultrasonic fingerprint sensor, wherein the second image is based on ultrasonic signals corresponding to a second time of flight range, the second time of flight range being delayed compared to the first time of flight range.
  • procedure 730 a difference in a width of ridges of the fingerprint pattern in the first image compared to the width of ridges of the fingerprint pattern in the second image is quantified.
  • procedure 730 is performed according to flow diagram 800 of FIG. 8.
  • a difference between the first image and the second image in one embodiment as shown at procedure 812 a difference image is generated by subtracting the second image from the first image.
  • a first signal strength is determined at a first spatial frequency range of at least one of the first image and the second image.
  • a second signal strength is determined at a second spatial frequency range of the difference between the first image and the second image.
  • the second signal strength is determined at the second spatial frequency range of the difference image between the first image and the second image in one embodiment, the first signal strength corresponds to a maximum in signal strength of the first spatial frequency range and wherein the second signal strength corresponds to a maximum in signal strength of the second spatial frequency range.
  • the second frequency range is distributed around a frequency substantially double a main frequency contribution of the first frequency range in one embodiment, the first spatial frequency range corresponds to a spatial ridge frequency range of the fingerprint pattern.
  • the first signal strength is compared to the second signal strength.
  • a ratio of the second signal strength to the first signal strength is determined. Provided the ratio satisfies a ratio range threshold, as shown at procedure 854, it is determined that the finger is a real finger. In one embodiment, the ratio range threshold is above 0.8.
  • the probability that the finger is a real finger is determined. In one embodiment, the probability that the finger is a real finger is based on the ratio of the second signal strength to the first signal strength, wherein the probability that the finger is a real finger Increases as the ratio of the second signal strength to the first signal strength increases.
  • procedure 730 is performed according to flow diagram 900 of FIG. 9.
  • procedure 910 of flow diagram 900 a first average width of ridges of the fingerprint pattern of the first image is determined.
  • procedure 920 a second average width of ridges of the fingerprint pattern of the second image is determined.
  • procedure 930 a difference between the first average width and the second average width is quantified.
  • a ratio of the first average width to the second average width is determined.
  • determining the probability whether the finger is a real finger includes comparing of the ratio of the first average width and the second average width to a width range threshold.
  • a method for determining whether a finger is a real finger at a fingerprint sensor comprising:
  • the second image is based on ultrasonic signals corresponding to a second time of flight range, the second time of flight range being delayed compared to the first time of flight range;
  • An ultrasonic fingerprint sensor device comprising:
  • processor configured to:
  • the second image is based on uitrasonic signals corresponding to a second time of flight range, the second time of flight range being delayed compared to the first time of flight range; quantify a difference in a width of ridges of the fingerprint pattern in the first image compared to the width of ridges of the fingerprint pattern in the second image;
  • a non-transitory computer readable storage medium having computer readable program code stored thereon for causing a computer system to perform a
  • the second image is based on ultrasonic signals corresponding to a second time of flight range, the second time of flight range being delayed compared to the first time of flight range;

Abstract

Selon l'invention, dans un procédé exécuté pour déterminer si un doigt est un doigt réel au niveau d'un capteur d'empreinte digitale ultrasonore, une première image d'un motif d'empreinte digitale est capturée au niveau d'un capteur d'empreinte ultrasonore, la première image étant basée sur des signaux ultrasonores correspondant à une première plage de temps de vol. Une seconde image du motif d'empreinte digitale est capturée au niveau du capteur d'empreinte ultrasonore, la seconde image étant basée sur des signaux ultrasonores correspondant à une seconde plage de temps de vol, la seconde plage de temps de vol étant retardée par rapport à la première plage de temps de vol. Une différence d'une largeur de crêtes du motif d'empreinte digitale dans la première image par rapport à la largeur de crêtes du motif d'empreinte digitale dans la seconde image est quantifiée. Sur la base de la différence quantifiée, une probabilité que le doigt soit un doigt réel est déterminée.
PCT/US2020/039208 2019-06-24 2020-06-23 Détection de faux doigt à l'aide de caractéristiques de crête WO2020263875A1 (fr)

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